Shared Control Research Articles

Driver–Automated Cooperation Driving Authority Optimization Framework for Shared Steering Control

In this paper, we introduce the preview-follower theory for modeling the trajectory-tracking controller of an automated system using model predictive control (MPC). The primary contribution of this research lies in enhancing tracking accuracy and driving safety when the driver and automated system share similar driving intentions, while also enabling a rapid transfer of driving authority to the human driver in cases of differing intentions. To verify the effectiveness of the proposed driving authority optimization framework, both simulation and driver-in-the-loop experiments were conducted under conditions of consistent and inconsistent driving intentions between the human driver and the autonomous driving system. The results of both experiments demonstrated that the proposed shared steering cooperative control and driving authority optimization framework not only significantly improves vehicle tracking accuracy but also promptly aligns with the driver’s intentions.

Lifespan of male and female APP/PS1 and APP NL-F/NL-F mouse models of Alzheimer's disease.

Alzheimer's disease (AD) disproportionately affects women, yet most preclinical research studies are male-centric. We performed lifespan analyses of male and female AD mouse models (APP/PS1 and APP NL-F/NL-F ) and their shared genetic background control (C57BL/6). Survival curves support significant sex differences between within genotypes. Minimal longevity revealed increased age in male APP/PS1, and decreased age in APP NL-F/NL-F mice. Maximal longevity revealed an increased average age in males. Furthermore, median lifespan differed between sex and genotype. This study supports sexual dimorphic survival in two mouse models of AD, emphasizing the need to examine mechanisms and treatments in both sexes.

Interactive strategy of shared control vehicle and automated vehicles based on adaptive non-cooperative game

Considering the complex traffic environment in the future, an interactive strategy between human-machine shared control vehicle (HSCV) and automated vehicles (AVs) based on the adaptive non-cooperative game (NCG) theory is proposed to ensure the safety of vehicles interaction control involving drivers with different characteristics. In HSCV, first, considering the environmental risk potential field (ERPF) and the human-machine conflict coefficient, an adaptive weight distribution method is designed to realize the weight distribution between a driver and an automation system. Subsequently, the steering angle command imposed by a driver is introduced into the game solving process between HSCV and AVs, enabling adaptive dynamic game through weight adjustment. During V2V interaction, based on NCG and distributed model predictive control, a motion planning controller (MOPC) is designed to integrate the optimal solution problem of path planning and tracking control and solve the interaction problem with constraints. Two typical interactive scenarios are used to verify the feasibility of the strategy. The results demonstrate that the interaction strategy considering the different drivers’ operating commands not only realizes the interaction between a driver and an ego vehicle but also realizes the interaction between an ego vehicle and nearby vehicles to ensure the safety of vehicles.

Distance and Preference for Full Versus Shared Control: The Moderating Role of Decision-Maker Self-Construal

Previous research on the impact of distance on the choice between full versus shared control international market entry has produced inconsistent results. The authors suggest that the characteristics of the decision-maker may be an important boundary condition, which helps explain this inconsistency. Specifically, the authors examine how differences in decision-maker self-construal influence the effects of distance on entry mode decisions. To investigate the phenomenon, the authors use multiple methods to conduct three separate studies, including two experiments and a meta-analysis. The results offer consistent evidence that the distance–entry mode relationship is stronger for managers with an independent self-construal than for managers with an interdependent self-construal.

Effect of Co-exposure to Additional Substances on the Bioconcentration of Per(poly)fluoroalkyl Substances: A Meta-Analysis Based on Hydroponic Experimental Evidence.

A consensus has yet to emerge regarding the bioconcentration responses of per(poly)fluoroalkyl substances under co-exposure with other additional substances in aqueous environments. This study employed a meta-analysis to systematically investigate the aforementioned issues on the basis of 1,085 published datasets of indoor hydroponic simulation experiments. A hierarchical meta-analysis model with an embedded variance covariance matrix was constructed to eliminate the non-independence and shared controls of the data. Overall, the co-exposure resulted in a notable reduction in PFAS bioaccumulation (cumulative effect size, CES = - 0.4287, p < 0.05) and bioconcentration factor (R2 = 0.9507, k < 1, b < 0) in hydroponics. In particular, the inhibition of PFAS bioconcentration induced by dissolved organic matter (percentage form of the effect size, ESP = - 48.98%) was more pronounced than that induced by metal ions (ESP = - 35.54%), particulate matter (ESP = - 24.70%) and persistent organic pollutants (ESP = - 18.66%). A lower AS concentration and a lower concentration ratio of ASs to PFASs significantly promote PFAS bioaccumulation (p < 0.05). The bioaccumulation of PFASs with long chains or high fluoride contents tended to be exacerbated in the presence of ASs. Furthermore, the effect on PFAS bioaccumulation was also significantly dependent on the duration of co-exposure (p < 0.05). The findings of this study provide novel insights into the fate and bioconcentration of PFAS in aquatic environments under co-exposure conditions.

A shared robot control system combining augmented reality and motor imagery brain–computer interfaces with eye tracking

Objective: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals.&#xD;Approach: A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency.&#xD;Main results:Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 minutes to complete the evaluation tasks. The success rate dropped below 0.5 when a 5-minute cutoff time was selected.&#xD;Significance: These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications&#xD;in the future.

Human Cognitive Learning in Shared Control via Differential Game With Bounded Rationality and Incomplete Information

Human Cognitive Learning in Shared Control via Differential Game With Bounded Rationality and Incomplete Information

On Covert Rate in Full-Duplex D2D-Enabled Cellular Networks With Spectrum Sharing and Power Control

On Covert Rate in Full-Duplex D2D-Enabled Cellular Networks With Spectrum Sharing and Power Control

Active Thermal Sharing Control for a Cascaded Converter in Medium-Voltage Applications

Active Thermal Sharing Control for a Cascaded Converter in Medium-Voltage Applications

Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements.

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human-robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.

Research on shared control of robots based on hybrid brain-computer interface

BackgroundWith the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain–computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control. New methodsThis paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion. ResultsEight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50 %, improves the success rate of traversing obstacles by 25.00 %, and the navigation trajectory is more consistent with expectations. ConclusionsThe HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.

Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders-such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury-by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot.

Implementing Model Predictive Control (MPC) in Steer-by-Wire Systems for Future Automated Vehicles

Abstract The need for future green sustainable transportation represents a motivation for the researchers in order to create a better society. In this paper a proposed Steer-by-Wire system for future automated vehicles is analysed by implementing model predictive control (MPC) controller that would optimise the desired behaviour of the vehicle. By using MATLAB/Simulink, a bicycle vehicle model is simulated in standardised test manoeuvres according to standard ISO:7401. These results are used as referent data for the desired trajectory of the vehicle and as input data for the MPC controller. The idea of using these results for the controller is generated by the desire of creating a shared information intelligent systems. Shared information and control in the future intelligent systems would be crucial for creating the desired sustainable transport. By traveling, the vehicle would collect data of the desired measured variables and those data would be send to every other vehicle. Therefore, the next vehicle would possess the previous data and would try to improve the vehicle response and safety. Every optimised trajectory would be shared with the rest of the vehicles and this cycle would result in further optimization and in creation of a safer, more comfortable and more sustainable transport. The optimisation is done by the MPC controller which is responsible for defining the steering wheel angle and therefore creating the Steer-by-Wire system that is entirely defined by the MPC controller without driver commands. By comparing the trajectory of the automated vehicle and the desired trajectory gained from the previous simulations, the desired steering wheel angle is defined. This results in creation of a Steer-by-Wire system that improves the vehicle dynamics, safety and dynamic response of the vehicle. The mathematical modelling, results of the simulations and advantages of using the MPC controller for the Steer-by-Wire system in automated vehicle are presented.

Feasibility Checking and Constraint Refinement for Shared Control in Assistive Robotics

Feasibility Checking and Constraint Refinement for Shared Control in Assistive Robotics

Comparative Study of the Regulation and Planning of National Capital Authority-Owned Enterprises in Realizing Sustainable Development of the Nusantara's Capital City

After the stipulation of Presidential Decree no. 62 of 2022 concerning the Nusantara Capital Authority, the Authority can have power over business entities which is characterized by share control and ownership of shares of a business entity so that it becomes the domain of the IKN Authority in its management. However, in reality, adequate regulations to accommodate the formation and planning of the Authority-Owned Business Entity itself are not not yet in place. This research aims to find out how business entities owned by the state capital are regulated and planned in a comparative perspective and what the ideal concept of regulation and planning of authority owned business entities is in realizing sustainable development of the Nusantara Capital City. The research used normative juridical with a statutory approach, comparative approach and conceptual approach. The research results show that the concepts of the three countries are different. The concept from Australia requires the formation of a state-owned enterprise through the law of its capital region, namely the Territorial State-owned Corporation Act 1990. Meanwhile, India uses a registration mechanism by injecting funds from the government to mark it as a government-owned company according to its level in accordance with the provisions of the Company Law of India, 2013. Then China acknowledged the existence of controlling and non-controlling shares in government control as regulated in the Law of State-owned Assets Enterprise 2018 Revision. In this case, Indonesia can adopt it by establishing an authority-owned business entity using regulations from the head of the authority that are regulatory and based on sustainable development. In this way, the next generation can experience the development results of the authority's business entities.

Human-AI Teamwork Interface Design Using Patterns of Interactions

Increasingly capable Artificial Intelligence (AI) agents are reaching new performance levels and humans need to work effectively with them in a variety of collaborative team structures. Of particular interest is “shared control collaboration”, where actions taken by the team depend on the blended combination of inputs from all team entities. We conducted an experiment where participants played a game and maneuvered a spacecraft while working in concert with an AI agent and simultaneously performing a secondary task. The experiment results (i) show different patterns of interactions across user interface designs, agent capabilities, and participants’ game experience, (ii) underscore the importance of providing information about the shared control collaborative task to humans who are working with a less-capable agent, and (iii) imply the potential of real-time communication about agent’s state to achieve better teamwork. Our findings are expected to be transferable to other shared control collaboration settings, including AI-enabled autopilots.

Healthy older adults generate transverse-plane momenta required for 90° turns while walking during the same phases of gait as used in straight-line gait

BackgroundGeneration and regulation (control) of linear and angular momentum is a challenge during turning while walking which may be exacerbated by age-related changes. In healthy older adults, little is known about how momentum is controlled during turns, especially within each phase of gait. Each phase of gait affords unique mechanical contexts to control momenta and regulate balance. In healthy young adults, we found that the transverse-plane linear and angular momenta generation strategies observed within specific phases of gait during straight-line gait were also used during turns. Therefore, in this study, we investigated whether healthy older adults shared similar momentum control strategies specific to each gait phase during straight-line gait and turns.MethodsNine healthy older adults completed straight-line gait and 90° leftward walking turns. We compared the change in transverse-plane whole-body linear and angular momentum across gait phases (left and right single and double support). We also compared the average leftward force and transverse-plane moment across gait phases.ResultsWe found that leftward linear momentum was generated most during right single support in straight-line gait and leftward turns. However, in contrast to straight-line gait, during leftward turns, average leftward force was applied across gait phases, with left single support generating significantly less leftward average force than other gait phases. Leftward angular momentum generation and average moment were greatest during left double support in both tasks. We observed some within-participant results that diverged from the group statistical findings, illustrating that although they are common, these momenta control strategies are not necessary.ConclusionsOlder adults generated transverse-plane linear and angular momentum during consistent phases of gait during straight-line gait and 90° turns, potentially indicating a shared control strategy. Understanding momentum control within each phase of gait can help design more specific targets in gait and balance training interventions.

CoplayingVR: Understanding User Experience in Shared Control in Virtual Reality

Shared control of avatars in virtual reality is emerging as a promising method to enhance collaborative interactions. While the literature investigates shared control as an input modality to understand the design of functional experiences, the user experience associated with shared control remains underexplored. In this paper, we investigate the user experience of shared control using a networked VR environment, "CoplayingVR". Through a user study comprised of three gaming tasks in VR with 48 participants, we collect both quantitative and qualitative data on the user experience and performance of shared control. Our findings reveal that the shared control mode significantly improved user experience factors and brought novelty to the play. Furthermore, shared control significantly enhanced the task efficiency for novice users. Finally, we discuss how our findings inform the implications for research and practice of shared control for the growing body of research in VR.

User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.

This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Complementary field-effect transistors (CFETs), which are structures in which different types of transistors are vertically stacked with a shared control gate, are being focused on for continuing to satisfy Moore's law by overcoming the limitations in pitch scaling. The structures of semiconductor devices become more complex as technology node shrinks, and interrelated multivariate parameters increase. In addition, predicting problems and proposing solutions by identifying complex patterns within extensive data collected for emerging semiconductor designs pose significant computational challenges and are inherently difficult. As a breakthrough in design technology co-optimization for advanced devices, this study developed a novel optimization framework integrating technology computer-aided design simulations, machine learning, and non-dominated sorting genetic algorithms. The developed framework provides unbiased optimal solutions, even in a high-dimensional objective space, while considering the tradeoff relationships between multiple variables. In addition, it enables inverse design to identify the design parameters of devices that satisfy specific electrical performance criteria using only a forward model, while achieving an error rate of less than 2%. Using this framework, we analyzed the operational mechanism of CFETs by comparing the inverse designs of various devices. This novel approach is particularly important when the design space is complex and extensive and is well suited for developing devices that emerge with technological advancements in the semiconductor industry.